Fitting three-level meta-analytic models in R: A step-by-step tutorial

By M. Assink, Carlijn J M Wibbelink. 2016

1,113 citations recorded by the source corpus.

Applying a multilevel approach to meta-analysis is a strong method for dealing with dependency of effect sizes. However, this method is relatively unknown among researchers and, to date, has not been widely used in meta-analytic research. Therefore, the purpose of this tutorial was to show how a three-level random effects model can be applied to meta-analytic models in R using the rma.mv function of the metafor package. This application is illustrated by taking the reader through a step-by-step guide to the multilevel analyses comprising the steps of (1) organizing a data file; (2) setting up the R environment; (3) calculating an overall effect; (4) examining heterogeneity of within-study variance and between-study variance; (5) performing categorical and continuous moderator analyses; and (6) examining a multiple moderator model. By example, the authors demonstrate how the multilevel approach can be applied to meta-analytically examining the association between mental health disorders of juveniles and juvenile offender recidivism. In our opinion, the rma.mv function of the metafor package provides an easy and flexible way of applying a multi-level structure to meta-analytic models in R. Further, the multilevel meta-analytic models can be easily extended so that the potential moderating influence of variables can be examined.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/95f645af7a355ecf2b9a7958c12734ad1768e44f

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Citations in the corpus

Advancing the social identity theory of leadership: A meta-analytic review of leader group prototypicality (2020, methodology): “…software (version 3.6.1; R Core Team, 2016) within the RStudio environment (version 1.1.463; RStudio Team, 2016) using the packages Metafor (Viechtbauer, 2010), plyr (Wickham, 2011), and QuantPsyc (Fletcher, 2012), following guidelines provided by Viechtbauer (2016) and Assink and Wibbelink (2016).”
Materialistic cues make us miserable: A meta‐analysis of the experimental evidence for the effects of materialism on individual and societal well‐being (2020, methodology): “The software used for the analyses was R with the package metafor and the function rma.mv() with restricted maximum‐likelihood, because this estimation method has been recommended by several authors for performing multilevel meta‐analyses (Assink & Wibbelink, 2016; Hox, 2010; Viechtbauer, 2010).”
A dose of nature: Two three-level meta-analyses of the beneficial effects of exposure to nature on children's self-regulation (2019, methodology): “All parameters of the three-level random effects models were estimated using the restricted maximum likelihood estimation, and the Knapp and Hartung (2003) method was used for calculating regression coefficients and confidence intervals (Assink & Wibbelink, 2016).”
Moving towards more sustainable aquaculture practices: a meta‐analysis on the potential of plant‐enriched diets to improve fish growth, immunity and disease resistance (2020, methodology): “…plant dosages tested), whose data were collected as different observations, we ﬁtted three-level meta-analytic models and investigated the distribution of variance over the three levels using R code adapted from Assink and Wibbelink (2016), where level 2 and level 3 were included as random-effects.”
Foster care placement instability: A meta-analytic review (2019, positive): “…conducted, and the more sophisticated three-level approach to meta-analysis has become available, which allows the inclusion of multiple effect sizes per study, the examining of both within and between study variability in effect sizes, and increases statistical power (Assink & Wibbelink, 2016).”
Recreational angler satisfaction: What drives it? (2021, methodology): “The meta- analyses were conducted in R with the Metafor package (Viechtbauer, 2010), using syntax from Assink and Wibbelink (2016).”